The Age of Scientific Wellness: Why the Future of Medicine Is Personalized, Predictive, Data-Rich, and in Your Hands

By Leroy Hood and Nathan Price

“If you want to understand how the latest advances in genomics and AI can completely transform your health, and to translate this promise into practical tools that you can apply today, read this book!”—Mark Hyman, author of Young Forever

Taking us to the cutting edge of the new frontier of medicine, a visionary biotechnologist and a pathbreaking researcher show how we can optimize our health in ways that were previously unimaginable.

We are on the cusp of a major transformation in healthcare—yet few people know it. At top hospitals and a few innovative health-tech startups, scientists are working closely with patients to dramatically extend their “healthspan”—the number of healthy years before disease sets in. In The Age of Scientific Wellness, two visionary leaders of this revolution in health take us on a thrilling journey to this new frontier of medicine.

Today, most doctors wait for clinical symptoms to appear before they act, and the ten most commonly prescribed medications confer little or no benefit to most people taking them. Leroy Hood and Nathan Price argue that we must move beyond this reactive, hit-or-miss approach to usher in real precision health—a form of highly personalized care they call “scientific wellness.” Using information gleaned from our blood and genes and tapping into the data revolution made possible by AI, doctors can catch the onset of disease years before symptoms arise, revolutionizing prevention. Current applications have shown startling results: diabetes reversed, cancers eliminated, Alzheimer’s avoided, autoimmune conditions kept at bay.

This is not a future fantasy: it is already happening, but only for a few patients and at high cost. It’s time to make this gold standard of care more widely available. Inspiring in its possibilities, radical in its conclusions, The Age of Scientific Wellness shares actionable insights to help you chart a course to a longer, healthier, and more fulfilling life.

• Language: English
• Publisher: Harvard University Press
• Release date: Apr 4, 2023
• ISBN: 9780674293458

Book preview

Introduction

This is a book about the future. It’s about the promises, pitfalls, and challenges we will have to overcome if we want to take the next big leap in human health. Not everyday progress that comes incrementally, but exponential improvement in the human condition. Our deeply held belief, which has emerged from our personal and scientific experiences, is that we are in the first stage of the largest paradigm shift in healthcare since the beginning of modern medicine—a time when the fundamental ways in which we approach health will change so profoundly that we will struggle to understand why we ever did things any other way.

Soon, we will be able to track and optimize the health trajectory of every individual throughout their life. We will be able to keep their bodies healthy and their minds young much longer than we do now. New technologies will make it possible to vastly improve the well-being of every person on this planet. (Yes, every person, for we should consider wellness to be a fundamental human right and strive to share these benefits broadly.)

This admittedly bold vision begins with a dismantling of our contemporary medical paradigm, a model that is really sickcare rather than healthcare. In today’s world, medical interventions happen long after a disease has taken hold, following a centuries-old medical playbook that goes like this:

Wait for something to go wrong.

Try to identify what caused the problem.

Try to fix it.

If it works, try the same approach on the next person.

If it doesn’t work, treat the complications—or write a death certificate and move along.

Even when the current model for care does work, significant damage has been done. We have long known that people who contract one disease are more likely to become sick with another. Diabetes increases the risk for dementia and coronary artery disease.¹ Cancer increases the risk of pulmonary thromboembolism.² And so it goes. It is true that risk factors for any one disease are potential starting places for many others. But there is another reality that physicians and researchers are beginning to recognize: our body is a system of systems, in which the perturbations caused by disease in one place can lead to seemingly unrelated problems in other places. It does us little good to view the human body in binary terms as either well (in which case medical care is unnecessary) or sick (in which case it is). Yet this is how our healthcare systems are built—not just in the United States but around the world. But there is a whole spectrum to wellness, and a long trajectory in the progression of disease to end-stage illness.

This book offers an alternative, a vision of twenty-first-century medicine that might meet and prevail over the vast medical challenges of our time. This vision begins with the dismantling of our current binary framing of wellness and disease in favor of the more nuanced, more accurate, and more commonsensical notion that each person’s state of health exists on a spectrum across wellness, transition, and disease.

It should go without saying that the healthcare of the future will seek to optimize the amount of time that we are well (Figure I.1). Yet contemporary medicine does very little in this regard. Most of us, as a result, are less healthy than we think. It is likely, in fact, that the physical and mental youthfulness, vigor, and resiliency that represent the wellness phase often only exists through the first ten to fifteen years of most people’s adult lives (perhaps as little as 20 percent of their adult life span).

If you are older than your twenties, it is very likely that you have already slipped out of a state of wellness and are in a phase of transition to disease and aging-associated declines. This phase can occupy a large portion of our lives, and yet it is all but completely ignored in the current healthcare paradigm. In fairness, this is partially because it has long been almost impossible to see these early wellness-to-disease transitions clearly. Soon, however, we will have the capacity to identify these transitions and safely intervene, reversing the movement toward disease and returning individuals to outright wellness. We will be able to do this again and again, throughout the course of a person’s life.

Figure I.1. The healthcare of the future will focus on extending wellness.

We (Lee and Nathan) are both optimists, but we do not foresee a future in which we beat back all diseases, altogether and forever. If and when a transition to fatal disease does happen, it should come swiftly—and as painlessly as possible—capping a full, productive, healthy, and happy life of overwhelming wellness. So how do we get there?

Wellness, Transition, and Disease

Our current health trajectories feature an early period of wellness followed by mostly imperceptible transitions, leading to the onset of diseases in the middle of life. (More than one in four adults in the United States has at least one diagnosable chronic condition by the age of 44, and about two-thirds will be diagnosed with a chronic disease by the time they reach the age of 65.)³ So it is that many of us spend half or more of our lives in a state of disease, whether we know it or not.

The idea that drives our new paradigm is to optimize each phase of the human health trajectory—lengthening periods of wellness and improving its quality, detecting and reversing transitions at the earliest possible moments, and greatly delaying (if not altogether avoiding) disease. We will do this through a data-driven process we call scientific wellness, an approach that will extend the wellness phase from the twenties to the eighties, and eventually even beyond.

There are three categories of information we need for every person to implement this data-driven vision of wellness and prevention. The first is the genome, the source code of life, which is virtually invariant throughout a person’s lifetime (save for the case of the mutations in cancer cells). Despite major advances in genomic analysis that have brought down the cost and time it takes to sequence an individual’s genome, fewer than 0.01 percent of the people on this planet have had their entire genome sequenced. Even though this only has to be done once, it’s a first hurdle on the path to data-driven health. Thankfully, this is now simple to do through numerous affordable commercial offerings, and hopefully soon will be a routine part of healthcare.

The second is the phenome, an assessment of your body’s status at any point in time during your life as a result of the interaction of your genome, your lifestyle, and your environment. Your phenome changes continuously, and it can be sampled at any time through certain measurements such as the gut microbiome and blood analytes, which are the proteins, metabolites, and other molecules that move through your body and mediate many of life’s functions, including energy production, nutrition, and cognition. Because the phenome is constantly changing, it needs to be sampled far more often than the genome—ideally, several times a year—and this is a rather big second hurdle.

The third category is digital measurements of health. This might be the easiest of the three, as there are already hundreds of millions of people worldwide collecting this kind of data on themselves, most often through the use of smartphones, watches, smart rings, and other wearables that can track their heart rate, body temperature, respiration, activity level, calories consumed and burned, sleep, menstrual cycles, blood sugar, hormone balances, and more.

With these three categories of data in hand, we can begin to assess the optimal physiology of one’s body and brain and detect early transitory phases many years—even decades—before a disease becomes clinically apparent.⁴ With this sort of lead time and a positive focus on optimizing health and resilience, we can use these same data to design and target personalized therapies that will end the transition long before disease materializes, when the pathological changes at hand are less complex and more reversible through interventions that are simpler, safer, and less intrusive.

You may have heard aspects of this bandied about in the past by medical practitioners who have suggested that early detection is a panacea for all that ails us. But as most physicians will attest, the pitch has always been better than the product, partly because such strategies are aimed at catching symptoms of disease, not signals of the early wellness-to-disease transitions. We have been trying to stop wildfires by watching for smoke on the horizon. But where there is smoke, there is already fire. If we get this right—and we cannot overstress how strongly we believe that we can—we will be able to intervene long before the blaze begins. One’s health trajectory will become a series of wellness phases punctuated by short transitions, immediately reversed, that will eventually be imperceptible from their general state of robustly good health. For the vast majority of our lives, we will be well beings.

Scientific wellness will ultimately allow us to conquer heart disease, the number-one killer in America today. It will make it possible to eliminate or vastly reduce incidents of diabetes or rheumatoid arthritis. It will be our ally in the so-called war on cancer. It will make the scourge of Alzheimer’s disease a distant memory. This may seem far-fetched, but we are on the cusp of a time when we will have the capacity to begin to eliminate most chronic diseases—though doing so will depend in part on people making choices in their own lives that will keep them well.

Let us be candid about how we see this revolution impacting future pandemic threats. Infectious diseases are almost always more dangerous—and in many cases exponentially deadlier—for individuals with pre-existing chronic conditions. This was the case with COVID-19, which sometimes killed indiscriminately but far more often took the lives of those who already had one or more chronic diseases, whether or not they had been formally diagnosed. So, scientific wellness will also be key to fighting the infectious diseases that will be an inevitable part of our future.

What would rapid and effective early diagnosis have meant for the deadly coronavirus pandemic? It might have saved hundreds of thousands of lives. COVID-19 was most lethal to those who already had one or more pre-existing conditions such as cardiovascular disease, chronic kidney disease, chronic lung diseases, diabetes, hypertension, obesity, and autoimmune diseases like rheumatoid arthritis and lupus.⁵ In the United States, older Americans represented about a third of the early cases, but they accounted for about half of intensive care admissions and more than three-quarters of deaths.⁶ Pre-existing conditions exacerbated the death toll. As the coronavirus pandemic overwhelmed healthcare systems around the world, patients who might have been saved with relatively basic critical care—the nearly 100-year-old technology known as a ventilator—were sometimes left to fend for themselves. There are no words to adequately describe the depth of this global tragedy. Yet it could have been ameliorated by earlier diagnoses and a better understanding of the possible disease trajectories.

If some of those pre-existing conditions had been addressed in the earliest stages of the wellness-to-disease process, how many lives could have been saved? Among those who eventually recovered, the path to health could have been so much smoother, and the draconian measures taken by much of the world largely avoided.

When it comes to infectious diseases, the best public health strategy is the one that will finally make a difference in the fight against chronic disease. This strategy is not just to be proactive by a matter of weeks or months; it is to be proactive by years and decades. A healthier population without pre-existing conditions is a population that will be less susceptible to the next pandemic, and the one after that, and the one after that. We, of course, collectively know that health is important, but until COVID-19, few viewed healthy sleep, exercise, and diet as active and pressing strategies to survive or minimize disease. One has only to look at the drastically slower progression of the SARS-CoV-2 virus in Japan, where diabetes is almost nonexistent, and hypertension and obesity are so much lower than in the United States. This is just the beginning of what scientific wellness informed by longitudinal data clouds promises—to detect the earliest wellness-to-disease transitions and, hopefully, reverse them at their earliest detectable stage. Wellness-centered healthcare throughout life is key to achieving these goals.

These bold proclamations come with some caveats. This future will not be realized if we follow a centuries-old strategy of seeking cures that are good for only some people, some of the time. Few people realize it, but the ten most popular drugs in the United States today—from esomeprazole and rosuvastatin to fluticasone and pegfilgrastim—work, collectively, for only about 10 percent of treated patients.⁷ Too many people are being subjected to their known side effects without benefit to their underlying condition. We will also fail to realize our goals if we ignore brain health, as is largely the case in contemporary medicine. A larger and larger share of the population is reaching the ninth and tenth decades of life, but often in such a state of mental frailty that these extra years are more of a burden than a blessing. This future will also fail to come about if we continue to treat sickness with the blunt force that even noninvasive treatments and therapies wield against disease. The side effects of medications and consequences of many procedures are often described by patients as a cure worse than the disease. And we will not realize this future if we stubbornly cling to the timeworn idea that a person’s chronological age is a suitable stand-in for assessing their state of biological aging—a hard-to-shake notion, even though it is so patently clear that not everyone ages at the same rate.

Once we dismantle these four damaging myths about human health, we can embrace a new standard for medical care that uses each person’s genetic profile and phenomic measurements to generate a unique list of actionable possibilities. In most cases, these proactive behaviors, verified by clinical studies, will either optimize wellness or prevent or forestall wellness-to-disease transitions in the body and brain. When a transition does occur, global, holistic, and data-driven approaches to this disease would inform a precise medical response, using massive data analysis to provide fundamental insights into how to approach therapy effectively for each individual.

All of this will offer the enticing opportunity for each of us to feel confident that our health spans—the years spent living in wellness without disease—will better align to our life spans. Ultimately, a life span and a health span should be virtually the same, meaning individuals would be able to live into their nineties or longer, maintaining an effective state of mental and physical wellness throughout these years.

This may sound like science fiction, but it is not a vision for some distant future. While it will obviously take time to change mainstream medicine, and every one of us will have to participate in our own health journey, we fully believe that the primary actions that must be taken to realize this goal can be completed in the next fifteen to twenty years. Indeed, many doctors and scientists are already taking meaningful steps toward applying the principles of scientific wellness to patient care and biomedical research, including the recent rapid growth of clinics dedicated to personalized medicine, functional medicine, integrative medicine, or healthy aging. They are embracing data from the major determinants of each person’s health—their genome, their lifestyle, and their exposures to the environment—to build a kind of care that is predictive, preventive, personalized, and participatory, what Lee was the first to call P4 medicine. And they are finding solutions for the major challenges of contemporary medicine: poor quality, exploding costs, the rapid aging of the patient population, and the dramatic increases in the number of individuals with one or more chronic diseases.

We will dive into some of these challenges throughout this book. Our goal is to help you see the power of scientific wellness, understand what it will take to build on nascent successes, and optimize your own individual health to ensure a long, productive, and healthy life. In fact, we suspect that what you learn in these pages will fundamentally change your view of what is possible for your own health. Our hope is that it will also help you see this way of thinking about health not simply as something that you can benefit from but as a structure for healthcare that can benefit everyone.

Getting to that point requires that we recall that medicine is about people. It’s about patients and physicians. It’s about researchers, healthcare administrators, and coverage providers. The current healthcare paradigm treats these as competing interest groups, but scientific wellness offers us an opportunity to focus everyone’s interests toward a new and mutually beneficial goal—a new standard of personalized medicine built for the twenty-first century and beyond.

We will not tell you that we can immediately shake the foundations of global healthcare and get everything right. We will not pretend that disruptive innovation is not going to be disruptive. That would be foolish. Where we see challenges ahead, we will name them and offer possible solutions. When we do not yet know the solutions, we will say so. It will be difficult. If it weren’t, it wouldn’t be revolutionary.

Both of us are actively participating in processes that will bring this vision to pass, and we both believe we’ll be around when it comes. For Lee, who is now in his mid-eighties, it might seem like a wistful fantasy. But we can see it coming, faster and faster, just over the horizon. To understand our confidence, perhaps it would be helpful to say a bit about the past.

We wrote this book together, and before we turn to the medical breakthroughs of the last ten years and what is coming next, we will share a bit about how we got here. For Lee, it all started a long time ago, in the small town of Shelby, Montana, thirty miles south of the Canadian border.

Lee’s Story: A Life in Science

There is a preconception that small, rural towns are poor places for children to grow up if they want to formulate big, world-changing ideas. The information revolution—which made it possible for anyone, anywhere, to access endless information—should have put this idea to rest a long time ago, but from my experience, it was never true to begin with.

While I think a lot about the future, I am not immune to the nostalgia that is common to people of my age. My thoughts often turn to my grandfather’s ranch in the shade of the Beartooth Mountains and to the town of Shelby, where I attended high school. When he wasn’t on his ranch, my grandfather built and managed a geology camp in the Beartooth foothills, where Ivy League professors brought their students for summer courses and projects. I learned a lot about science from the students and faculty at that camp. In my junior year of high school, I completed a geological map of an oil-producing anticline in northern Wyoming that got me invited to the Westinghouse Science Talent Search in Washington, DC. It was the first time I’d left Montana, and I went by myself on the Great Northern Railway. I was awed by the brilliant students I met in the capital and returned to Montana determined to join their ranks.

My father worked for Mountain States Bell, managing the construction of a series of communication microwave repeater stations across the state. He was a superb engineer, and he taught weeklong summer courses to his employees in general aspects of electrical engineering. He encouraged me to take these courses—mostly, I think, so he could show me off to his employees. I participated with reluctance, as I would rather have been out hiking and mountaineering. In retrospect, these courses changed my life. They taught me to think about biology in terms of engineering systems and circuits—a concept that was later very useful for my commitment to developing new technologies for biology and the new discipline of systems biology.

Shelby High School had only 146 students, but it had just about everything any other school might offer. I played oboe in the school band and was co-editor of the yearbook. I acted in plays and traveled the state as part of the debate team. I served in student government and played quarterback on a football team that was undefeated for two and a half years. And I was blessed with some of the best teachers I would ever have. They treated me as a colleague and broadened my intellectual horizons. They challenged me to think about science as a career with a sophistication I never would have achieved alone.

My most formative experience was helping my chemistry teacher, Clifford Olsen, teach a sophomore biology class. I taught directly from articles out of Scientific American, and in the spring of 1956, I taught a lesson I developed from an article on the structure of DNA—only three years after it was discovered by James Watson, Francis Crick, Maurice Wilkins, and Rosalind Franklin. I didn’t understand much about it, but the idea that the core of biology was centered around this beautiful molecule fascinated me then as it does to this day.

DNA intrigued me for another reason. My brother Glenn, who was six years younger than me, was born with Down syndrome, and when I learned about DNA, I wondered if it might play a role in his condition. My father and mother were split on how best to care for him. There was a state home for children with Down syndrome in Boulder, a good four hours away, in central Montana. That’s where Dad thought Glenn would get the best care. Mom wanted her boy to be at home. Dad, as usual, won the argument, and it was probably the right decision; Glenn flourished, and in his teen years moved to Hardin, Montana, where he spent the rest of his life. Glenn went on to own his own home and hold three jobs simultaneously for much of his adult life.

I respected my brother and desperately wanted to understand Down syndrome. I remember asking our physician and my parents about the cause of Glenn’s condition. No one had an answer. And I was always fascinated by questions without answers.

The genetic cause of Down syndrome, a duplication of all or a portion of chromosome 21, was finally discovered in 1959. By that time, I was at the California Institute of Technology (Caltech), following my mentor, Mr. Olsen, who had attended university there during World War II as a Navy meteorologist. He was so impressed with the school he had vowed to send any good science student he had there. At Caltech, I was surrounded by outstanding students, many of whom were already quite a bit further along in their math and science than I was. I had to work hard to catch up.

Caltech gave me a superb technical education in math, chemistry, and physics. My professors included Linus Pauling, the only man ever to win two unshared Nobel Prizes, and Richard Feynman, another Nobel Prize winner, whose work was foundational to quantum mechanics. Both were excellent and inspiring teachers. My biology training was exceptional, too, but it focused almost entirely on microbes, plants, and viruses. I was passionately interested in human biology and disease, and decided to go to medical school in the hope of later doing research in human biology. At Johns Hopkins Medical School in Baltimore, I became transfixed with molecular immunology and the question of how our immune systems could protect us against so many different types of pathogens. One approach to this problem was to study the structure of antibody molecules, a major component of the human immune response. I decided to pursue my PhD in this field, returning to Caltech so that I could work under William (Bill) Dreyer, who had made fascinating discoveries in this area of science.

Dreyer was an unusual biologist because he had a deep interest in technology. He gave me two dicta that have guided me ever since: (1) if you want to practice biology, practice it at the leading edge, and (2) if you want to transform a field, invent a new technology to interrogate it.

The Vietnam War dictated the next phase of my life. As an MD, I had two choices: I could join the military or serve in the public health service. I chose the latter and was assigned to the National Institutes of Health (NIH), where I met many young scientists who would become leaders in the field of medicine over the next fifty years. I also learned to manage my own laboratory. But mostly I decided what I wanted to do in the next stage of my career: study human biology and disease.

And so it was that I headed back across the country to Caltech where, in 1970, I became an assistant professor in biology. I had decided to focus on two areas of research. The first was molecular immunology, a leading-edge topic area in biology at that time. It was the complexity of this field, which deals with disease responses mediated at the DNA level, that so intrigued me—all those challenging questions without answers embedded in the complexity of human biology. But I immediately saw a problem: we didn’t have the tools to approach many of these questions. In my emerging view, biology was an information science; without ways of measuring this information, we were lost. This led me to my second focus, the development of new technologies to assess the four major types of biological information: DNA, RNA, proteins, and biological networks (per Bill Dreyer’s second dictum, above, which identifies technological innovation as the path to transformative research).

Medicine in those days, and even today, was very much like the parable of the elephant and the six blind men. In this ancient Buddhist story, each man feels a different part of the elephant and comes to a completely different conclusion about what the animal is based on what he has felt. The man who grabs the trunk believes he is touching a snake, while the one who grabs a leg thinks he is touching a tree trunk. Limited by the symptoms of illness, physicians were like the blind men. I came to several conclusions in my time at Caltech. First, I felt it was important to generate lots of data on each individual, for buried in the data were the keys to deciphering human complexity. Second, I became convinced that blood was a window into viewing wellness and disease, because it bathes all organs and receives protein signals from each organ that can reflect the internal health state of that organ. In principle, the wellness-to-disease states of all organs can be assessed from blood. It wasn’t until recently that we had the tools to start doing this and understand all that we could learn. Moreover, at the time of my studies, we didn’t have the language to convey the complexity of all this biological information or understand how to think about it. We weren’t just blind; we were speechless, too. Later, systems biology gave us a language for beginning to decipher human complexity.

Around the time I was coming to these realizations, in the early 1970s, I read The Structure of Scientific Revolutions by Thomas Kuhn, which described paradigm changes in physics. These shifts—revolutionary new ways of thinking about or practicing a discipline—are difficult to conceptualize and even harder to achieve. That’s because discoveries that come to be recognized as paradigm-shifting moments almost always face staunch resistance initially. Scientists, like the rest of us, are generally reluctant to give up long-held beliefs and accept new ideas.

In spite of the resistance—or perhaps because of it—I have been fortunate to have been involved in a number of paradigm-shifting moments in my career: changes in biology, medicine, and technology that we now see as revolutionary.⁸ In homage to my boyhood home, I’ve come to think of these as Big Sky moments. They led me to a new approach to biology founded on the realization that humans employ complex biological systems to carry out the normal functions of the body, and that focusing on any one of them to the exclusion of the others will not get us very far.

Bringing Engineering to Biology

Twenty years after the discovery that DNA molecules exist in the form of a three-dimensional double-stranded helix—a finding that gave us the conceptual insights we needed to understand what our genome does—we still didn’t have effective tools to explore this code. DNA is a digital code with a four-letter alphabet—the bases G, C, T, and A. By the time I started teaching at Caltech, we knew that DNA was the source code of all life, and we were beginning to use this nascent realization to better understand how humans develop from a single fertilized egg into an adult human. For this to happen, all 20,000 units of DNA encoded in our genes have to be copied into another four-letter language, messenger RNA (or mRNA). These single-stranded molecules are then translated into the twenty-letter amino-acid language of proteins—the functional machines of life and a key part of the biological networks through which our bodies operate. Since the information they carry is encoded in subunits in DNA, RNA, or proteins, one must be able to determine the order of these nucleic acid bases and characterize the order of amino acids in a protein to understand the nature of this linear information. This is what we now call sequencing.

Pehr Edman, a brilliant Swedish biochemist, found a way to sequence proteins in the 1940s. He was the first person to build an automated sequencer, which sped up the process considerably. His tools and methods, however, required lots of proteins and could not generate long sequence reads. When I returned to Caltech, sequencing remained a long and labor-intensive process. Since my initial training was in protein chemistry, I thought I might be able to develop an instrument that could sequence proteins more efficiently, offering longer sequence reads with far less starting material. If I could achieve this, proteins available in very small quantities could then be characterized, their genes cloned and sequenced, and, hopefully, their missions discovered. This was not simply a challenge of biology but also one of chemistry and engineering.

There was a lot of pushback to such cross-disciplinary work in those days. For some biologists, it was anathema. In 1973, Caltech’s chairman of biology, Robert Sinsheimer, came into my office and asked me to give up on technology development. Your field is molecular immunology, he reminded me. That’s where your focus should be.

I told him I wouldn’t be altering my goals and waited to see what the fallout might be. Sinsheimer later told me he was delivering the message on behalf of the school’s senior biology professors, who felt it was inappropriate for me to practice engineering in a biology department. If that was my focus, they had suggested, I should be moved to the engineering department. To his credit, Sinsheimer never sought to have me transferred.

The resistance didn’t just come from Caltech. When I sought support for my team’s automated DNA sequencer from the National Institutes of Health, my first two grant applications received priority scores that were among the worst I would ever get. The reviewers offered comments like this approach is impossible or graduate students can easily do all of the sequencing needed. They seemed to have little appreciation for the exponentially increasing amount of sequencing that would soon be the bedrock of so much biological and medical research—and even less for how talented graduate students could most effectively use their time.

I wish I could say only a few outliers held this view. I remember talking with Jim Watson, the co-discoverer of the structure of DNA, in the mid-1980s about automated DNA sequencing. Why are you putting so much time and resources into this project? he asked me. I tried my best to explain, convinced that automated DNA sequencing would transform biology, but I don’t think I succeeded. Just remember, he said sarcastically, that there are a billion people in China, and if each one of them sequenced just three bases of the human genome, it would be finished.

My initial efforts were only marginally successful, and there were times when I thought the naysayers might be right. But in the mid-1970s, a brilliant chemist and engineer, Michael Hunkapiller, joined my lab. We collaborated with Bill Dreyer, my former PhD mentor, to develop an idea that Dreyer had conceived for protein sequencing—a liquid-gas phase instrument. This approach eventually led to a technology that could create long sequence reads with 200 times less protein than previously required.

Over the next twenty-five years, my collaborators and I worked across disciplinary boundaries to develop six different instruments to analyze and synthesize DNA and proteins in various ways. These included automated DNA and protein sequencers, automated DNA and peptide synthesizers, the very large-scale, inkjet-based DNA synthesis technology, and the single RNA molecule NanoString analysis technology.⁹ The automated DNA sequencing instrument approach employed four different fluorescent dyes, one for each DNA letter. With a synthesis sequencing technique that allowed us to visualize each base in a DNA sequence, we were able to convert the order of colors obtained by laser scanning into the sequence of the DNA fragment by a computational transformation. The four-color DNA sequencing chemistry we developed at Caltech has been the cornerstone of automated DNA sequencing for the past thirty-five years.

At last, we could read this sacred text, the code of life—and we had the combined forces of molecular biology, chemistry, engineering, and computer science to thank.

The Human Genome Project

Now that we had the tools to sequence DNA more efficiently, we could focus on a previously impossible goal: determining the order of the four nucleotide bases of a DNA strand in each of the twenty-three pairs of human chromosomes—what came to be known as the sequence of the genome. This was a daunting task, as about three billion DNA letters make up the human genome, and chromosomes range in size from about 50 million to 175 million base pairs.

This was not a mountain to climb just for the sake of being the first. For decades, scientists had been dreaming of what they might learn if DNA were ever to become more easily readable. Once the order of bases in the human genome was determined, many believed, we would have the information we needed to begin to understand how different genes are expressed in different tissues—to understand why a liver cell is different from a brain cell and so forth. There was also a widespread hope that one could correlate defective genes with different disease states and better understand the mechanisms of disease so that we could develop targeted therapies to deal with them more effectively. Writing in the journal Science in 1986, Renato Dulbecco of Caltech suggested that sequencing the human genome might be the key to understanding